High-throughput time-lapse microscopy is an excellent way of studying gene function by collecting time-resolved image data of the cellular responses to gene perturbations. With the increase in both data amount and complexity, computational methods capable of dealing with large image data sets are required. While image processing methods have been successfully applied to endpoint assays in the past, the analysis of complex time-resolved read-outs was so far still too immature to be applied on a large-scale. Here, we present a complete computational processing pipeline for such screens. By automatic image processing and machine learning, a quantitative description of phenotypic dynamics is obtained, from the raw bitmaps. In order to visualize the resulting phenotypes in their temporal context, we introduce Event Order Maps allowing a concise representation of the major tendencies of causes and consequences of phenotypic classes. In order to cluster the phenotypic kinetics, we propose a novel technique based on trajectory representation of multidimensional time series. We demonstrate the use of these methods applying them on a genome wide RNAi screen by time-lapse microscopy. (C) 2009 Elsevier Inc. All rights reserved.